Determining a state of a vehicle on a road

ABSTRACT

The present invention relates to determination of a state of a vehicle on a road portion. The vehicle includes an Automated Driving System (ADS) feature. At first, map data associated with the road portion, positioning data indicating a pose of the vehicle on the road, and sensor data of the vehicle are obtained. Then, a plurality of filters for the road portion are initialized. Further, one or more sensor data point(s) in the obtained sensor data is associated to a corresponding map-element of the obtained map data to determine one or more normalized similarity score(s). Now, based on the determined one or more normalized similarity score(s), one or more multivariate time-series data are also determined and provided as input to a trained machine-learning algorithm. Then, one of the initialized filters is selected by the machine learning algorithm to indicate a current state of the vehicle on the road portion.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application for patent claims priority to European PatentOffice Application Ser. No. 22173553.3, entitled “DETERMINING A STATE OFA VEHICLE ON A ROAD” filed on May 16, 2022, assigned to the assigneethereof, and expressly incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to methods and systems for determining astate of a vehicle on a road. More specifically, embodiments and aspectsof the present disclosure relate to initialization of filters for thevehicle on a road and systems and methods for selection of theinitialized filters by means of machine learning algorithms to determinethe state of the vehicle on the road.

BACKGROUND

During the last few years, the research and development activitiesrelated to autonomous vehicles have exploded in number and manydifferent approaches are being explored. An increasing portion of modernvehicles have advanced driver-assistance systems (ADAS) to increasevehicle safety and more generally road safety. ADAS—which for instancemay be represented by adaptive cruise control (ACC) collision avoidancesystem, forward collision warning, etc.—are electronic systems that mayaid a vehicle driver while driving. Today, there is ongoing research anddevelopment within a number of technical areas associated to both theADAS and the Autonomous Driving (AD) field. ADAS and AD will herein bereferred to under the common term Automated Driving System (ADS)corresponding to all of the different levels of automation as forexample defined by the SAE J3016 levels (0-5) of driving automation, andin particular for level 4 and 5.

In a not too distant future, ADS solutions are expected to have foundtheir way into a majority of the new cars being put on the market. AnADS may be construed as a complex combination of various components thatcan be defined as systems where perception, decision making, andoperation of the vehicle are performed by electronics and machineryinstead of a human driver, and as introduction of automation into roadtraffic. This includes handling of the vehicle, destination, as well asawareness of surroundings. While the automated system has control overthe vehicle, it allows the human operator to leave all or at least someresponsibilities to the system. An ADS commonly combines a variety ofsensors to perceive the vehicle's surroundings, such as e.g. radar,LIDAR, sonar, camera, navigation system e.g. GPS, odometer and/orinertial measurement units (IMUs), upon which advanced control systemsmay interpret sensory information to identify appropriate navigationpaths, as well as obstacles, free-space areas, and/or relevant signage.

An important requirement for autonomous and semi-autonomous vehicles isthat they are able to estimate the pose i.e. the state (position andorientation) of the vehicle with accuracy and consistency since this isan important safety aspect when the vehicle is moving within traffic.Conventionally, satellite based positioning systems (Global NavigationSatellite Systems, GNSS), like for instance Global Positioning System(GPS), Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS),Galileo, Beidou, have been used for positioning purposes.

However, these and other regional systems are often not accurate enoughto rely on solely for determining the pose of a moving vehicle inautonomous applications. Moreover, GNSS based solutions have even lessaccuracy in determining height information.

Alternatively, there are systems and methods which utilize HD-mapinformation together with a number of different sensors to increase thereliability of the map position such as cameras, LIDAR, RADAR, and othersensors for determining vehicle travelling parameters such as speed,angular rate and so on. However, even given current vehicle pose, it isstill hard to predict a robust vehicle pose estimation by only odometrydue to the measurement noise from different measurement sensors, e.g.motion sensors.

There is thus a need in the art for new and improved solutions fordetermining the state of the vehicle on the road with more certainty andaccuracy.

SUMMARY

It is therefore an object of the present invention to provide a system,a vehicle comprising such a system, a method, and a computer-readablestorage medium, which alleviate all or at least some of the drawbacks ofpresently known solutions.

More specifically, it is an object of the present invention to alleviateproblems related to determination of state of a vehicle comprising anAutomated Driving System (ADS) feature on a road portion having two ormore lanes.

These objects are achieved by means of a system, a vehicle comprisingsuch a control system, a method, and a computer-readable storage medium,as defined in the appended independent claims. The term exemplary is inthe present context to be understood as serving as an instance, exampleor illustration.

According to a first aspect of the present invention, there is provideda method for determining a state of a vehicle on a road portion havingtwo or more lanes, the vehicle comprising an Automated Driving System(ADS) feature. The method comprises obtaining map data associated withthe road portion and obtaining positioning data indicating a pose of thevehicle on the road and obtaining sensor data from a sensor system ofthe vehicle. Further the method comprises initializing a plurality offilters for the road portion wherein one filter is initialized per laneof the road portion based on the obtained map data, the obtainedpositioning data, and the obtained sensor data, wherein each filterindicates an estimated state of the vehicle on the road portion.Additionally, the method comprises associating one or more sensor datapoint(s) in the obtained sensor data to a corresponding map-element ofthe obtained map data and determining one or more normalized similarityscore(s) between the associated obtained map data and the obtainedsensor data. Further, the method comprises determining one or moremultivariate time-series data based on the determined one or morenormalized similarity score(s), wherein each multivariate time-seriesdata is attributed to a corresponding initialized filter among theplurality of initialized filters. In addition, the method comprisesproviding the one or more multivariate time-series data as input to atrained machine-learning algorithm. The trained machine learningalgorithm is configured for determining a confidence probability valuefor each initialized filter of the plurality of initialized filters bymeans of a probabilistic classifier. Further the machine learningalgorithm is configured for selecting one of the initialized filters, bycomparing the confidence probability values determined for eachinitialized filter in conjunction with one or more multi-objectiveoptimized coefficient(s), each optimized coefficient being indicative ofan optimization, e.g. an optimized trade-off, between a readinessperformance indicator and an accuracy performance indicator forselecting a single initialized filter as an output of the machinelearning algorithm. The output of the machine learning algorithm isindicative of a current state of the vehicle on the road portion. Themethod further comprises controlling the ADS feature of the vehiclebased on the selected initialized filter.

When it comes to autonomous vehicles, an accurate localization of thevehicle state is of great importance in order to make safe decisionswithout endangering a vehicle's occupants or external objects,particularly when using the ADS features.

According to the presented method, by employing a data-driven approachcomprising the use of machine learning algorithms to identify and selectthe most promising initialized filter out of the plurality ofinitialized filters per lane of a multi-lane road, the possibilities ofaccurately and efficiently estimating the state of the vehicle on theroad portion are noticeably improved. This advantage is particularlynoteworthy in comparison with rule-based algorithm designs foridentifying the most accurate initialized filter indicative of the stateof the vehicle on the road portion. Even though the rule-basedapproaches may be capable of accurately determining the state of thevehicle on the road portion, the likelihood of avoiding unforeseeablecorner cases is considerably enhanced by training and employing themachine learning algorithm according to the present invention.

To this end, the trained machine learning algorithm is used to influenceand promote behavior that leads to an increased possibility ofgenerating interesting scenarios, including the corner case scenariosinvolving multiple environmental variables or conditions happeningsimultaneously or outside the conventional levels. Further, theversatility of the proposed solution establishes the proposed method,and corresponding system and vehicle to be readily adaptable for varyingtraffic situations or road and transportation infrastructure indifferent countries.

According to some embodiments, each initialized filter may be one of aBayesian filter and a combination of multiple Bayesian filters. Inseveral embodiments, each Bayesian filter may be one of Kalman Filter,Extended Kalman Filter, EKF, Unscented Kalman Filter, UKF, CubatureKalman Filter, CKF, and Particle Filter, PF.

In various embodiments, the obtained sensor data may compriseinformation about a state of one or more other vehicles in thesurrounding environment of the vehicle, lane marker geometry, lanemarker type, traffic sign information, road barrier information, andInertial Measurement Unit, IMU, data. In various embodiments the mapdata may comprise HD-map data.

In some embodiments, the method may further comprise determining one ormore normalized similarity score(s) between the associated obtained mapdata and the obtained sensor data by computing an association cost valuefor each sensor data point of the one or more sensor data point(s)associated to a corresponding map element; and selecting a sensor datapoint and map-element combination having the smallest association costvalue.

In several embodiments, the method may further comprise determining theone or more multivariate time-series data based on the determined one ormore normalized similarity score(s) by obtaining one or moretime-dependent feature(s) of each determined normalized similarityscore.

In some embodiments, the trained machine learning algorithm may befurther configured for sorting the determined confidence probabilityvalues for the plurality of the initialized filters based on theconfidence level of each determined confidence probability value.

In several embodiments, for each optimized coefficient the readinessperformance indicator may comprise any one of an availabilityperformance indicator comprising a proportion of the one or moremultivariate time-series data for which a selection of a singleinitialized filter is performed by the trained machine learningalgorithm. The readiness performance indicator may further comprise anearliness performance indicator comprising an average fraction passed ofthe one or more multivariate time-series data before a selection of asingle initialized filter is performed by the trained machine learningalgorithm. Further for each optimized coefficient the performanceaccuracy indicator may comprise a proportion of correctly-selectedsingle initialized filters by the trained machine learning algorithm,being indicative of the current state of the vehicle on the roadportion.

According to a second aspect of the present invention there is provideda (non-transitory) computer-readable storage medium storing one or moreprograms configured to be executed by one or more processors of aprocessing system, the one or more programs comprising instructions forperforming the method according to any one of the embodiments of themethod disclosed herein.

The term “non-transitory,” as used herein, is intended to describe acomputer-readable storage medium (or “memory”) excluding propagatingelectromagnetic signals, but are not intended to otherwise limit thetype of physical computer-readable storage device that is encompassed bythe phrase computer-readable medium or memory. For instance, the terms“non-transitory computer readable medium” or “tangible memory” areintended to encompass types of storage devices that do not necessarilystore information permanently, including for example, random accessmemory (RAM). Program instructions and data stored on a tangiblecomputer-accessiblestorage medium in non-transitory form may further betransmitted by transmission media or signals such as electrical,electromagnetic, or digital signals, which may be conveyed via acommunication medium such as a network and/or a wireless link. Thus, theterm “non-transitory”, as used herein, is a limitation of the mediumitself (i.e., tangible, not a signal) as opposed to a limitation on datastorage persistency (e.g., RAM vs. ROM).

According to a third aspect of the present invention, there is provideda computer program product comprising instructions which, when theprogram is executed by one or more processors of a processing system,causes the processing system to carry out the method according to anyone of the embodiments of the method disclosed herein.

According to a further fourth aspect, there is provided a system fordetermining a state of a vehicle on a road portion having two or morelanes, the vehicle comprising an Automated Driving System, ADS, feature,the system comprising processing circuitry configured to obtain map dataassociated with the road portion and to obtain positioning dataindicating a pose of the vehicle on the road and to obtain sensor datafrom a sensor system of the vehicle. Further, the processing circuitryis configured to initialize a plurality of filters for the road portionwherein one filter is initialized per lane of the road portion based onthe obtained map data, the obtained positioning data, and the obtainedsensor data, wherein each filter indicates an estimated state of thevehicle on the road portion. Even further, the processing circuitry isconfigured to associate one or more sensor data point(s) in the obtainedsensor data to a corresponding map-element of the obtained map data anddetermine one or more normalized similarity score(s) between theassociated obtained map data and the obtained sensor data. In addition,the processing circuitry is configured to determine one or moremultivariate time-series data based on the determined one or morenormalized similarity score(s), wherein each multivariate time-seriesdata is attributed to a corresponding initialized filter among theplurality of initialized filters. The processing circuitry isadditionally configured to provide the one or more multivariatetime-series data as input to a trained machine-learning algorithm. Thetrained machine learning algorithm is configured to determine aconfidence probability value for each initialized filter of theplurality of initialized filters by means of a probabilistic classifierand select one of the initialized filters, by comparing the confidenceprobability values determined for each initialized filter in conjunctionwith one or more multi-objective optimized coefficient(s), eachoptimized coefficient being indicative of an optimization between areadiness performance indicator and an accuracy performance indicatorfor selecting a single initialized filter as an output of the machinelearning algorithm indicative of a current state of the vehicle on theroad portion. The processing circuitry is further configured to controlthe ADS feature of the vehicle based on the selected initialized filter.

According to yet another fifth aspect, there is provided a vehiclecomprising one or more vehicle-mounted sensors configured to monitor asurrounding environment of the vehicle. The vehicle further comprises alocalization system configured to monitor a pose of the vehicle i.e.geographical position and heading of the vehicle on a road. The vehiclefurther comprises a system according to the fourth aspects and variousembodiments of the fourth aspect. The vehicle further comprises an ADSfeature for controlling one or more of acceleration, steering, andbraking of the vehicle.

Further embodiments of the different aspects are defined in thedependent claims.

It is to be noted that all the embodiments, elements, features andadvantages associated with the first aspect also analogously apply tothe second, third, fourth and the fifth aspects of the presentdisclosure.

These and other features and advantages of the present disclosure willin the following be further clarified in the following detaileddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

Further objects, features and advantages of embodiments of thedisclosure will appear from the following detailed description,reference being made to the accompanying drawings. The drawings are notto scale.

FIG. 1 shows a schematic top view of a road portion having multiplelanes and a vehicle traveling on the road portion in accordance withsome embodiments.

FIG. 2 shows a schematic block diagrams of a machine learning algorithmin accordance with several embodiments.

FIGS. 3 a-b are schematic flowcharts illustrating a method in accordancewith several embodiments.

FIG. 4 shows a schematic side view illustration of the vehiclecomprising the control system in accordance with some embodiments.

DETAILED DESCRIPTION

Those skilled in the art will appreciate that the steps, services andfunctions explained herein may be implemented using individual hardwarecircuitry, using software functioning in conjunction with a programmedmicroprocessor or general purpose computer, using one or moreApplication Specific Integrated Circuits (ASICs) and/or using one ormore Digital Signal Processors (DSPs). It will also be appreciated thatwhen the present disclosure is described in terms of a method, it mayalso be embodied in one or more processors and one or more memoriescoupled to the one or more processors, wherein the one or more memoriesstore one or more programs that perform the steps, services andfunctions disclosed herein when executed by the one or more processors.

In the following description of exemplary embodiments, the samereference numerals denote the same or similar components. Even thoughthe following disclosure mainly discusses vehicles in the form of cars,the skilled reader readily realizes that the teachings discussed hereinare applicable to other forms of vehicles such as trucks, buses andconstruction equipment.

FIG. 1 illustrates a schematic perspective top views of a vehicle 1comprising an Automated Driving System (ADS). Moreover, the ADScomprises one or more ADS features that are preferably a level 2 featureor higher according to SAE J3016 levels of driving automation foron-road vehicles. In the present context, an ADS feature may be in theform of an autopilot feature, a traffic jam pilot, a highway pilot, orany other SAE J3016 level 2+ ADS feature. The vehicle 1 may also bereferred to as the ego-vehicle.

The vehicle 1 comprises a control system 10 for controlling a driversupport function (i.e. an ADS feature) for autonomously maneuvering thevehicle 1 according to several embodiments and aspects of the presentdisclosure. The control system 10 may be a part of the overall ADSarchitecture of the vehicle, and may accordingly be a module orcomponent of the ADS. The control system 10 of the vehicle 1 comprisescontrol circuitry 11 or processing circuitry 11 configured to obtaindata comprising information about the surrounding environment of thevehicle 1. The vehicle is also provided with a localization system 5which in communication with the control system 10 are configured toprovide an estimation of the vehicle's 1 state or pose i.e. vehicle'sgeographical position and heading on the road portion 24. The termobtaining is herein to be interpreted broadly and encompasses receiving,retrieving, collecting, acquiring, and so forth.

The state of the vehicle in the context of this disclosure can beconstrued as having three physical states, namely the longitude, thelatitude and the heading of the vehicle. The longitude and the latitudeare defined with respect to a geographical coordinate system such as theCartesian coordinate system and indicate the longitudinal position andlateral position of the vehicle on the road portion. The heading of thevehicle indicates the compass direction of the vehicle with respect tothe geographical north 120 and is typically understood as an angle (θ)between a vector 100 of a forward-orientation of the vehicle and acenter line 110 extending from the vehicle towards the geographicalnorth. The state of the vehicle may also be referred to as a pose of thevehicle. The pose is in some embodiments represented by a 2D Cartesianposition and a yaw of the vehicle (x, y, θ). However, in someembodiments, the pose is a 6D pose where the position is defined by a 3DCartesian position and the orientation is defined by a roll, pitch, andyaw of the vehicle.

FIG. 1 shows the ego-vehicle 1 travelling on a portion 24 of a road. Inthis example, the road is in the form of a carriage way having fourlanes 101-104, and the road portion 24 is a portion of the carriage way.In several other examples and embodiments the road may be any other typeof road e.g. a highway with dual carriageways. The road may also be amotorway, freeway or expressway. The road may also be a country road,rural road or any other carriageway. The road may have a plurality oflanes such as more than one lane in the same travelling direction e.g.two or more lanes or at least one lane in each travelling direction asis usually the case for rural roads.

The control system 10 of vehicle 1 is configured to determine thegeographical position and heading of the vehicle on the road portion 24based on data from the localization system 5 comprising positioning dataindicating a pose, i.e. position and orientation, of the vehicle on theroad portion 24, map data associated with the road portion 24 and sensordata obtained by the from a perception system i.e. sensor system 6 ofthe vehicle 1. In several embodiments, the vehicle may utilize alocalization system 5 in the form of a suitable satellite basedpositioning systems, such as either one of a GNSS or a correspondingregional system such as e.g. a GPS, Globalnaya NavigazionnayaSputnikovaya Sistema (GLONASS), Galileo, Beidou, etc.

The localization system 5 may comprise or be associated with an HD-mapmodule. An HD-map is in the present context to be understood as mapcomprising data with highly accurate and realistic representations ofthe road travelled upon by the vehicle 1. In more detail HD-maps may beunderstood as maps that are particularly built for autonomous drivingpurposes. These maps have an extremely high precision, oftentimes at acentimeter-level. Moreover, the maps generally contain information suchas where the lanes are, where the road boundaries are, where the curvesare, how high the curves are, and so forth.

The control system 10 may for in various aspects and embodimentscomprise or be associated with an Inertial Measurement Unit (IMU). AnIMU may be understood as a device configured to detect linearacceleration using one or more accelerometers and rotational rate usingone or more gyroscopes. Thus, in some embodiments, the sensor data maybe in the form of sensor data obtained from the IMU. The output from theIMU is then used to estimate a change in the vehicle's pose over time.In more detail, the prediction of the vehicle's pose may be estimatedbased on a vehicle motion model together with motion sensor data (e.g.data from accelerometers and gyroscopes, which will herein collectivelybe referred to as motion sensors). The obtained sensor data mayadditionally comprise information about a state of one or more otherexternal vehicles in the surrounding environment of the ego-vehicle,lane marker geometry on the two or more lanes of the portion 24 of theroad, lane marker 241-243 type (e.g. solid, dashed, double marker, etc.)on the portion 24 of the road, traffic sign information 245, roadbarrier information, etc.

In various aspects and embodiments, the prediction of the pose of thevehicle performed by the control system 10 may comprise using linear ornon-linear filtering e.g. by using a Bayesian filter or a combination ofmultiple Bayesian filters. In several aspects and embodiments, eachBayesian filter may be one of Kalman Filter, Extended Kalman Filter(EKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), andParticle Filter (PF). The selection of the Bayesian filters may be basedon design factors or quality of obtained sensor data e.g. the linearityof the sensor measurement models which may control the use of suitablefilters or filter combinations for different sensors.

In an example shown in FIG. 1 , the localization system 5 of the vehicle1, obtains a GNSS position of the vehicle 1 on the road portion 24. Thisposition is marked as an initial position “A” of the vehicle 1 and asingle filter comprising a Bayesian filter or a combination of Bayesianfilters are initialized based on the initial position of vehicle 1 aswell as the HD-map data of the road portion 24 together with the sensordata to predict the state of the vehicle 1 of the road portion. Inseveral embodiments, the obtained positioning data may comprise aninitial longitude, initial latitude and an initial heading of thevehicle 1 connected to the initial GNSS position “A” of the vehicle.Realistically, the GNSS data is usually associated with an uncertainty“AA” indicated by the dashed circle in FIG. 1 .

The inventors have realized that by initializing multiple filters201-204 for the multi-lane road portion 24 of FIG. 2 such that onefilter is initialized per lane 101-104 of the road portion 24, thesafety and certainty of the pose estimation of the vehicle 1 on the roadportion may be increased noticeably. Even though, this approach requiresmore computation and processing compared to initializing a single filteraround the initial GNSS position, it however allows for a much safer andmore certain decision making, especially for controlling the ADS featureof the ego-vehicle 1. In the context of the present disclosure, theoutcome or output of each filter 201-204 may also be referred to as ahypothesis. Each hypothesis therefore is an estimation of the pose orstate of the vehicle 1 on a designated lane among the plurality of lanes101-104 of the road portion 24. The inventive method and systempresented here therefore scrutinizes each hypothesis to select a mostpromising hypothesis indicative of the most accurate state of thevehicle on the road portions.

As shown in FIG. 1 , by initializing a filter 201-204 per lane 101-104on the road portion 24, each filter continuously provides an estimationof the state of the vehicle 1 on its designated lane based on thepositioning data, obtained sensor data and the HD-map data. Each filtermay comprise a Bayesian filter or a combination of Bayesian filters.This way, the most accurate initialized filter amongst the plurality ofinitialized filters 201-204 can be identified and selected which in turnwill indicate the most accurate pose of the vehicle 1 on the roadportion 24.

In several embodiments, the initial longitudinal position, initial laterposition and initial heading (initial pose) comprised in the positioningdata connected to the initial position “A” of the vehicle may beobtained from a satellite positioning module, wherein the satellitepositioning module may use a Kalman filter or any variants of a Kalmanfilter such as an extended Kalman filter, an unscented Kalman filter, ora cubature Kalman filter, to continuously estimate the vehicle's posewith inputs of GNSS data, and a predefined motion model of the vehicle.This way initial positioning data of the vehicle 1 comprising initiallongitude, initial latitude and initial heading may be obtained. In aprediction stage, the motion model may be used together with thevelocity and/or acceleration data e.g. as obtained from the IMU topredict the vehicle's pose. The continuously-obtained positioning datae.g. GNSS data may be applied to the Kalman filter to further estimateand update the vehicle's pose. An output of the satellite positioningmodule is geodetic vehicle pose, including the initial heading, initiallongitude, initial latitude, or in some embodiments an initial altitudeof the vehicle.. In several embodiments, a pose converter may be used totransform the initial vehicle pose (output of the satellite positioningmodule) from geodetic coordinates to a local Cartesian coordinatesystem. As a result, the initial vehicle pose can be represented as alongitudinal position, a lateral position, and a heading.

It should be noted that the filter initialization is designed to be acontinuous process for controlling the ADS feature of the ego-vehicle 1and it is possible that in some scenarios, all or some of theinitialized filters and their respective pose estimations will beterminated and a filter re-initialization in all or some of the laneswill be repeated.

However, to select the most accurate initialized filter amongst theplurality of initialized filters 201-204, it is required to establishalgorithms which will efficiently perform the selection process.

The inventors have further realized that by using a data-driven approachcomprising the use of machine learning (ML) algorithms to identify andselect the most promising initialized filter out of the plurality ofinitialized filters of a multi-lane road, the possibilities ofaccurately estimating the state of the vehicle on the road portion 24improves significantly. Further, the data-driven approach of the presentdisclosure is much more scalable and easier to maintain than anyrule-based approach or any algorithm based on human intuition.

Accordingly, hypothesis-inference algorithms and methods according tothe present disclosure comprise machine learning algorithms used toinfluence and promote behavior that increases the possibility foraccurately estimating the state of the vehicle in various scenariosincluding corner case or edge case scenarios involving multipleenvironmental variables or conditions happening simultaneously oroutside the conventional levels. Trained machine learning algorithms inthe present context may comprise supervised machine learning algorithms,trained, tested and verified based on conventional real-world data whichis obtained through driving the vehicle 1 on various types of roadsunder a variety of environmental conditions and for suitable periods oftime to collect and evaluate the data sets for various scenarios.Particularly, the process of training the ML algorithm may comprisespre-processing, training, testing, and validating phases. To this end,HD-map data, positioning data, various sensor measurements includingcamera, radar, LIDAR, GNSS, IMU, pose of other external vehicles in thesurrounding environment of the ego-vehicle 1, geometry and type of lanemarkers, traffic signs and traffic information, road barriers, weatherforecast, etc. may be employed as input data for training the MLalgorithm. Further, reference data, also referred to as “ground-truth”data from the real-world driving data collection which include theactual poses and trajectories of the ego-vehicle may be used. In someaspects and embodiments, unsupervised machine learning algorithms may beused for at least part of the determination of the most accuratehypothesis. In some cases for instance parameters of the trainedsupervised machine learning algorithm may be used as the initial statesfor a continuous unsupervised machine learning model based on the largescale dataset.

In several embodiments and aspects in order to assess the likelihood ofthe difference hypotheses and to be able to select the most promisinghypothesis among the plurality of initialized hypotheses, the sensormeasurements (e.g. state of one or more other external vehicles in thesurrounding environment of the vehicle, lane marker geometry, lanemarker type, traffic sign information, road barrier information, IMUdata etc.) acquired by the sensor devices are converted into a series ofquality measures. This may include associating one or more sensor datapoint(s) in the obtained sensor data to a corresponding map-element ofthe obtained map data on the HD-map. To perform such as association ofsensor measurements to the map data, both the map elements and thesensor measurements may be converted into a same coordinate system basedon the believed pose of the ego vehicle. Further, the association ofeach sensor measurement i.e. sensor data point associated with eachsensor measurement to a map element may further comprise determining oneor more similarity score(s) between the associated obtained map data andthe obtained sensor data. Determining the one or more similarityscore(s) between the associated obtained map data and the obtainedsensor data may be performed by computing an association cost value foreach sensor data point of the one or more sensor data point(s)associated to a corresponding map element and selecting a sensor datapoint and map-element combination having the smallest association costvalue. In some embodiments if no association could be made or thesmallest association cost would be significantly high, a default penaltyis assigned to the sensor data point in question e.g. by labeling thesensor data point as an outlier. After the sensor data point-map elementassociations are made, each association cost is normalized based on thenumber of sensor data points that were used to provide a normalizedsimilarity score(s) between the associated obtained map data and theobtained sensor data. The normalization may be performed by means ofknown approaches in the art e.g. by using a Chi-square distribution,with the number of sensor data points corresponding to the degrees offreedom of the Chi-square distribution. The normalized similarity scoresmay also be referred to as “measurement model qualities” or simply as“model qualities” herein, each measurement model quality beingindicative of the similarity between the HD-map elements and perceivedsensor data points e.g. sensor measurements of the lane markers relativeto each initialized filter 201-204. Each measurement source may have itsown model and thus its own measurement model quality. For example, lanemarker geometry as a feature represents how well the geometry of thelane markers perceived by the sensors of the vehicle 1 follow theexpected geometry as displayed by the HD-map. Using a Chi-squarenormalization ensures that all model qualities have similar magnitudes,regardless of their number of sensor data points. This way, it is easierto combine model qualities together and compare hypotheses. In someexemplary embodiments the model qualities may be obtained at a frequencyof 40 Hz, however the model qualities may be obtained at any othersuitable frequencies.

Further, with reference to FIG. 2 , in order to provide the requiredinput 201 to the supervised machine learning algorithm 200 for selectingthe most accurate initialized filter, the model qualities are used fordetermining one or more multivariate time-series data 201 based on thedetermined one or more normalized similarity score(s), wherein eachmultivariate time-series data may be attributed to a correspondinginitialized filter among the plurality of initialized filters. In thepresent context by the multivariate time-series it is meant atime-series in which each data point is multi-dimensional. In FIG. 2 ,the number of one or more multivariate time-series data equals “n” whichis also the number of active hypotheses. By way of example, for the roadportion 24 of FIG. 1 , n would be equal to 4, since there are 4 lanes onthe road portion and each lane has a designated initialized hypothesis201-204. Clearly the number of hypothesis and the attributed input 201may vary accordingly depending on each scenario.

In FIG. 2 the input 201 to the machine learning algorithm 200 comprisesmultivariate time-series data,

MTS

_t{circumflex over ( )}iϵ[1, n], for “i” being the number of activehypotheses at a current time step “t”, wherein i=1, . . . , n.

Determination of the multivariate time-series data may be done based onraw time-series data, or it may be done using some feature extraction ortransformation method. Examples of features that can be extracted fromtime-series data of the model qualities may be temporal averages, meanof values, the variance, standard deviation, the mode of the data orcombinations or aggregations of the model qualities. For example, one ofthe features may be constructed by taking the mean of the two lanemarker type model qualities temporally and averaging them into onevalue. Another feature may be the average variance for all modelqualities until a given time step. Any other examples of extraction offeatures from the model qualities than the ones mentioned above maysimilarly be performed. The feature extraction or transformation methodsmay be any of the known methods or processes in the art e.g. randomfeature transformation by use of random convolutional kernels, orextracting the mean, the standard deviation and the slope of the datawithin specific time windows. Each

MTS

_t{circumflex over ( )}iϵ[1, n]may be a vector of dimension t×m, whereinm equals the number of features obtained from the quality models.

Other example of feature extraction and transformation may be useddepending on the type of data and type of an implemented probabilisticclassifier which are assumed to be readily available to the skilledperson.

The trained machine learning algorithm in several embodiments andaspects may comprise a supervised machine learning classifier 205, alsoreferred to as a probabilistic classifier herein. In variousembodiments, various probabilistic classifiers known in the art may beused such as a Gaussian Naive Bayes, Logistic Regression, CanonicalInterval Forest (a state-of-the-art model for multivariate time-seriesclassification), Linear Support Vector Classifier, Kernel Support VectorClassifier, tree ensemble classifiers such as Gradient BoostingClassifier (a popular and widely used model) or Random ForestClassifier, etc. Each of the above-mentioned machine learningprobabilistic classifiers 205 are accordingly trained and evaluated tobe implemented in the hypothesis inference algorithm presented in thisdisclosure. Accordingly, by providing the one or more multivariatetime-series data as input 201 to the trained machine-learning algorithm200, a confidence probability value 203 for each initialized filter ofthe plurality of initialized filters may be determined by means of thetrained probabilistic classifier 205. The confidence probability values203 in FIG. 2 comprises “n” probability values p_(1,) p_(2,) . . . ,p_(n), each calculated for one of the “n” active initialized hypothesisbased on the input data

MTS

_t{circumflex over ( )}iϵ[1, n].

As mentioned above, the supervised probabilistic classifier may beapplied to each hypothesis of the plurality of hypotheses at given timesteps “t”. The input fed into the probabilistic classifier aretime-dependent features of model qualities i.e. the one or moremultivariate time-series data,

MTS

_t{circumflex over ( )}iϵ[1, n], determined based on the determined oneor more normalized similarity score(s). The input data may also bereferred to as a driving sequence herein, each driving sequencecomprising the one or more concurrent multivariate time-series data,each multivariate time-series data being attributed to its correspondinginitialized filter.

The output of the probabilistic classifier is a floating probabilityvalue between “0” and “1” for each initialized hypothesis. Stateddifferently, various features extracted from the time-series data of themodel qualities as explained earlier may be fed into the supervisedprobabilistic classifier in order to calculate the confidenceprobability values for each of the hypotheses. The calculated confidenceprobability value for each hypothesis i.e. initialized filter is anindicative of accuracy of that specific hypothesis representing thestate of the ego vehicle 1 on the road portion. In other words, thetrained machine learning algorithm ensures that highest probablehypothesis is selected. This task is fulfilled by a function 207comprised in the trained machine learning algorithm for assessing theconfidence in selecting the most accurate initialized hypothesis basedon the output of the probabilistic classifier. This function may also bereferred to as a trigger function 207 herein. The trigger function maybe a binary function, providing a “true” output class 217 whichsignifies that a hypothesis is correct, and a “false” output class 219which signifies that a hypothesis is incorrect. The probabilisticoutputs of the probabilistic classifier fed into the trigger functionwould lead to a belief of the trigger function expressing if ahypothesis is part of the “true” output class or not.

Accordingly, the purpose of the trigger function is deciding if theoutput from the probabilistic classifier is reliable enough, i.e. “true”output class 217, that it should make a prediction, i.e. selection, ofthe most accurate hypothesis. Further the trigger function may comprisea “reject” or “false” output class 219 functionality. That is, where thetrigger function may not reach a prediction based on the outputsreceived from the probability classifier. In these scenarios the triggerfunction 207 is configured to wait for more data to be received from theprobabilistic classifier. The time 213 shown in FIG. 2 indicates thatwhen the trigger function 207 could not reach a certain hypothesisselection at the current time “t”, it may wait for another time step“Δt” in order to gather further data and evaluate the newly generateddriving sequence in the time step “t+Δt”. This way a temporal aspect ofthe problem is taken into account, meaning that by waiting for a longerperiod of time the multivariate time-series data of the model qualitiesmay provide more information regarding the surroundings of the egovehicle which could in turn be used for a more confident assessment inselecting the most accurate hypothesis by the trigger function. Thus,the trigger function 207 is implemented by providing a loop of gatheringdata, evaluating hypotheses, and then either outputting a selection orgathering more data. If the trigger function 207 deems the prediction ofthe single initialized filter to be reliable enough, then thathypothesis with highest probability is selected as the output 215 of themachine learning algorithm 200.

In some exemplary embodiments the multivariate time series data may besegmented into driving sequences of a certain time length e.g. 30seconds. With the model qualities obtained at a frequency of 40 Hz, eachdriving sequence may be segmented into 1200 time steps with each1-second time length corresponding to 40 time steps. Accordingly, eachdriving sequence may be evaluated at certain lengths of time, forinstance after 1 second has passed. With the probabilistic classifieroutputting its first output at the current time step 40 (t=40)corresponding to 1 second at the 40 Hz rate, if the trigger functionprovides a “true” class output 217 and a selection is made, then thetrigger function is only run once for that driving sequence. Otherwise,it will be run at least once more waiting for more data to arrive at thesubsequent time steps (t=t+Δt). It should be clear to a skilled personin the art that the above mentioned scenario forms only an exampleaccording to some embodiments and any other suitable parameter valuesmay be used instead.

In some embodiments the trigger function may be implemented as aparametrized linear inequality function. In various embodiments andaspects one of the initialized filters having the highest confidenceprobability may be selected by the trained machine learning algorithm bycomparing the confidence probability values 203 determined for eachinitialized filter. Further, one or more multi-objective optimizedcoefficient(s) 209 may be introduced to the trigger function 207. Eachoptimized coefficient 209 is indicative of an optimization, e.g. anoptimized trade-off, between a readiness performance indicator and anaccuracy performance indicator for selecting a single initialized filteras an output of the machine learning algorithm. In some exemplaryembodiments the trigger function may make use of the highest probabilityoutput of the classifier i.e. the most probable hypothesis and thesecond highest probability output of the classifier i.e. the second mostprobable hypothesis and the difference between these two parameters forevaluating the driving sequences in conjunction with the multi-objectiveoptimized coefficients. Other suitable combinations of the outputs ofthe probabilistic classifier may also be used by the trigger functionfor selecting the most suitable hypothesis.

The selected initialized filter is indicative of a current state of thevehicle 1 on the road portion 24. A comparison of the probabilisticoutputs is what is used to determine the certainty with which aselection of the single initialized filter can be made. In someembodiments the outputs of the probabilistic classifier may be sorted211 by magnitude and used as input to the trigger function whose outputdetermines if a selection should be made at this time step or if themodel needs more data, i.e. time, to become more confident in that thehighest probable hypothesis, given by the probabilistic classifier, iscorrect.

There are a few advantages to optimize over the two objectivesseparately instead of merging them together into one objective. Firstly,with a single objective, there may be a need to weight the importance ofthe readiness and accuracy in advance, which may be difficult in somesituations. Moreover, no consideration needs to be made regarding thescaling of the measures, which wouldn't be the case if they were to beoptimized together. Finally, the results from a multi-objectiveexecution give a clearer picture of the readiness-accuracy trade-offwithout having to run the algorithm several times as would be the casein the single-objective scenario.

In some embodiments the readiness performance indicator may comprise anavailability performance indicator. In some embodiments the readinessperformance indicator may comprise an earliness performance indicator.

The multi-objective optimized coefficients of the trigger function arelearned in the training phase of the ML algorithm by optimizing bothaccuracy and readiness i.e. availability and/or earliness performancemeasures. In simple terms, the earliness performance measure accountsfor making a prediction of an initialized filter as fast as possible.The availability performance measure accounts for making a prediction ofan initialized filter in as many scenarios as possible. In other words,availability is defined as the proportion of driving sequences for whichthe trigger function provides a “true” output evaluation before thesequence has ended i.e. before a set time step e.g. 1 second and no moreevaluations can be made. The accuracy performance measure simplyaccounts for a proportion of correctly-selected single initializedfilters by the trained machine learning algorithm i.e. the most accurateinitialized filter indicative of the current state of the vehicle 1 onthe road portion 24. Thus, a prediction of the most accurate hypothesisfor a sequence of input data may be provided as soon as a prediction canbe made with certainty, bearing in mind that the more observations aremade, the more certain a prediction would be. The problem to be solvedby the multi-objective algorithm then becomes finding the trade-offbetween accuracy and readiness. The coefficients of the trigger functionare established based on the multi-objective optimization in thetraining phase of the trained machine learning algorithm and the learnedcoefficients are then used in the test stage to find the best hypothesisin conjunction with the confidence probability values outputted by theprobabilistic classifier.

In several embodiments and examples, the multi-objective optimizationalgorithm may be implemented by using a genetic algorithm and morespecifically NSGA2 algorithm. NSGA2 stands for (Non-Dominated SortingGenetic Algorithm 2) which is a genetic algorithm for producingnon-dominated solutions in multi-objective optimization. To choose oneof the solutions from the non-dominated solution set received, anachievement scalarization function as known in the art may be used. Insummary, an optimal solution will be returned based on a vector ofweights for accuracy and availability, as well as for accuracy andearliness performance indicators. In various embodiments and examples,the weights may be set by customizing the preference for accuracy versusavailability or earliness. For instance, the weights may be set bychoosing the accuracy performance indicator to be more important thanavailability and/or earliness measures. In various embodiments, thetraining of the probabilistic classifier, the optimization of thetrigger function and testing of the complete trained ML model may be aniterative process performed continuously on the obtained data sequences.

FIGS. 3 a-b show flowcharts of a method 300 according to various aspectsand embodiments of the present disclosure for determining a state of avehicle 1 on a road portion 24 having two or more lanes 101-104. Themethod 300 comprises obtaining 301 map data, associated with the roadportion 24. As mentioned earlier the map data is typically the HD-mapdata comprising data with highly accurate and realistic representationsof the road portion 24. The method further comprises obtaining 303positioning data 402 indicating a pose of the vehicle 1 on the road.More specifically, the initial GNSS position “A” of the vehicle 1 on theroad portion 24 is obtained by the localization system 5. In severalembodiments, the positioning data comprises an initial longitude,initial latitude and an initial heading of the vehicle 1 connected tothe initial GNSS position “A” of the vehicle.

The control system 10 is configured to perform the method step ofobtaining 305 sensor data from a sensor system i.e. perception system 6of the vehicle 1 comprising a variety of different sensors such assensors 6 a-6 c illustrated for the vehicle 1 of FIG. 4 .

In some embodiments obtaining 305 sensor data may comprise obtaining 314at least one of acceleration and velocity of the vehicle 1. By obtainingthe velocity and/or acceleration data as well as a temporal positioningprofile of the vehicle 1, an in-lane longitudinal position (x) of thevehicle 1 on the road portion 24 can be determined.

Further, in several aspects and embodiments, obtaining 305 sensor datamay comprise obtaining 316 the lane marker geometry and a relativedistance of the vehicle 1 from the lane markers 241-243 on the roadportion 24. By retrieving the information about the lane markers on theroad portion and calculating the relative distance of the vehicle fromdifferent lane marker types (e.g. the lane markers located on a rightside and/or on a left side of the vehicle), an in-lane lateral position(y) and in-lane heading 100 of the vehicle 1 on the road portion 24 maybe determined. By determining the longitudinal and in-lane lateralposition of the vehicle, the heading of the vehicle can be determined.To determine the heading 100 of the vehicle, a tangent of the angle “θ”along the center line 110 (being substantially parallel with theextension of at least one of the lanes on the road portion 24) of thevehicle is calculated. As a result, the direction of the tangent 100along the lane is determined as the initialization for the heading ofthe vehicle. The method further comprises initializing 307 a pluralityof filters 201-204 for the road portion wherein one filter isinitialized per lane 101-104 of the road portion 24 based on theobtained map data, the obtained positioning data, and the obtainedsensor data, wherein each filter 201-204 indicates an estimated state ofthe vehicle 1 on the road portion 24. As mentioned earlier each filtermay employ a Bayesian filter or a combination of different Bayesianfilters suitable for the specific scenario to estimate the state of thevehicle 1. In some embodiments, the initializing 307 of the filters201-204 per lane 101-104 of the road portion 24 may further comprise,for each filter continuously obtaining 304 the positioning data of thevehicle 1 on the road over time. In other words, obtaining the initialGNSS position “A” of the vehicle 1 on the road portion 24 is aniterative process and the localization system 5 is configured torepeatedly acquire the initial position “A” over a certain period oftime, in predetermined intervals, or based on any other suitabletemporal scheme. This way the certainty of the obtained positioning datais noticeably elevated which contributes to reducing the margin of errorwhen estimating the vehicle pose by each filter 201-204. In someembodiments each filter may be initialized based on the determined 307a, 307 b longitudinal position (x) and in-lane lateral position (y) andheading 100 of the vehicle 1 on the road portion for each filter201-204. The method continuous to FIG. 3 b wherein it is shown that themethod 300 further comprises associating 309 one or more sensor datapoint(s) in the obtained sensor data to a corresponding map-element ofthe obtained map data. Even further, the method 300 comprisesdetermining 311 one or more normalized similarity score(s) between theassociated obtained map data and the obtained sensor data. The method300 further comprises determining 313 one or more multivariatetime-series data based on the determined one or more normalizedsimilarity score(s), wherein each multivariate time-series data isattributed to a corresponding initialized filter among the plurality ofinitialized filters. Additionally, the method comprises providing 315the one or more multivariate time-series data as input 201 to a trainedmachine-learning algorithm 200. The trained machine learning algorithm200 is configured for determining 317 a confidence probability value 203e.g. including p_(1,) p_ (2,) p_(3,) p_(4), for each initialized filterof the plurality of initialized filters 201-204 (4 filters in theexample of FIG. 1 , with n=4) by means of a probabilistic classifier205. Further, the machine learning algorithm is configured for selecting319 one of the initialized filters, by comparing the confidenceprobability values determined for each initialized filter in conjunctionwith one or more multi-objective optimized coefficient(s) 209, eachoptimized coefficient being indicative of an optimization, e.g. anoptimized trade-off, between a readiness performance indicator and anaccuracy performance indicator for selecting a single initialized filteras an output 215 of the machine learning algorithm. The output of themachine learning algorithm is indicative of a current state of thevehicle on the road portion. The method further comprises controlling321 the ADS feature of the vehicle based on the selected initializedfilter.

In some embodiments and aspects the method 300 may further compriseobtaining 323 a signal indicative of a desired activation of the ADSfeature. Controlling 321 the ADS feature may comprise activating 325 theADS feature after the selection of the initialized filter has been madeand using the selected initialized filter to indicate the vehicle'sstate on the road portion 24. Accordingly, controlling 321 the ADSfeature may comprise at least controlling 321 one or more ofacceleration, steering, and braking of the vehicle.

In some embodiments the method 300 may further comprise determining 311the one or more similarity score(s) between the associated obtained mapdata and the obtained sensor data by computing 327 an association costvalue for each sensor data point of the one or more sensor data point(s)associated to a corresponding map element and selecting 329 a sensordata point and map-element combination having the smallest associationcost value.

In several embodiments, the method 300 may further comprise determining313 the one or more multivariate time-series data based on thedetermined one or more normalized similarity score(s) by obtaining 331one or more time-dependent feature(s) of each determined normalizedsimilarity score.

Executable instructions for performing these functions are, optionally,included in a non-transitory computer-readable storage medium or othercomputer program product configured for execution by one or moreprocessors.

In some embodiments, the trained machine learning algorithm may furtherbe configured for sorting 333 the determined confidence probabilityvalues for the plurality of the initialized filters based on theconfidence level or magnitude of each determined confidence probabilityvalue.

In several embodiments for each multi-objective optimized coefficientthe readiness performance indicator may comprise an availabilityperformance indicator comprising a proportion of the one or moremultivariate time-series data for which a selection of a singleinitialized filter is performed by the trained machine learningalgorithm, i.e. the trigger function. In other words the availabilityperformance measure accounts for making a prediction of an initializedfilter in as many scenarios as possible. In some embodiments, selectionof a single initialized filter for a proportion of the one or moremultivariate time-series data may be performed within or before a setperiod of time, as discussed earlier with respect to time steps.

In several embodiments for each multi-objective optimized coefficientthe readiness performance indicator may comprise an earlinessperformance indicator comprising an average fraction passed of the oneor more multivariate time-series data before a selection of a singleinitialized filter is performed by the trained machine learningalgorithm i.e. the trigger function. In other words the earlinessperformance measure accounts for making a prediction of an initializedfilter as fast as possible.

Further, in several embodiments, the accuracy performance indicator maycomprise a proportion of correctly-selected single initialized filtersby the trained machine learning algorithm, i.e. the trigger function,being indicative of the current state of the vehicle on the roadportion. In other words, the accuracy performance measure simplyaccounts for a proportion of correctly-selected single initializedfilters.

FIG. 4 is a schematic side view of a vehicle 1 comprising a controlsystem 10 (control device 10) for determining a vehicle pose. Thevehicle 1 further comprises a perception system 6 and a localizationsystem 5. A perception system 6 is in the present context to beunderstood as a system responsible for acquiring raw sensor data from onsensors 6 a, 6 b, 6 c such as cameras, LIDARs and RADARs, ultrasonicsensors, and converting this raw data into scene understanding. Inparticular, the vehicle 1 has at least one vehicle-mounted camera 6 cfor capturing images of (at least a portion of) a surroundingenvironment of the vehicle. The localization system 5 is configured tomonitor a geographical position and heading of the vehicle, and may inthe form of a Global Navigation Satellite System (GNSS), such as a GPS.However, the localization system may alternatively be realized as a RealTime Kinematics (RTK) GPS in order to improve accuracy. Moreover, in thepresent context the vehicle 1 is assumed to have access to a digital map(e.g. a HD-map), either in the form of a locally stored digital map orvia a remote data repository accessible via an external communicationnetwork 20 (e.g. as a data stream). In some embodiments, the access tothe digital map may for example be provided by the localization system5.

The control system 10 comprises one or more processors 11, a memory 12,a sensor interface 13 and a communication interface 14. The processor(s)11 may also be referred to as a control circuit 11 or control circuitry11 or processing circuitry 11. The control circuit 11 is configured toexecute instructions stored in the memory 12 to perform a method fordetermining a state of an ADS-equipped vehicle on a road portion havingone or more lanes according to any one of the embodiments disclosedherein. In more detail, the processing circuitry 11 is configured toperform the method steps of the method 300 in FIGS. 3 a-b and withreference to FIG. 2 to select a single initialized filter by the MLalgorithm on the multi-lane 101-104 stretch of road 24. The memory 12 ofthe control device 10 can include one or more (non-transitory)computer-readable storage mediums, for storing computer-executableinstructions, which, when executed by one or more computer processors11, for example, can cause the computer processors 11 to perform thetechniques described herein. The memory 12 optionally includeshigh-speed random access memory, such as DRAM, SRAM, DDR RAM, or otherrandom access solid-state memory devices; and optionally includesnon-volatile memory, such as one or more magnetic disk storage devices,optical disk storage devices, flash memory devices, or othernon-volatile solid-state storage devices.

Further, the vehicle 1 may be connected to external network(s) 20 viafor instance a wireless link (e.g. for retrieving map data). The same orsome other wireless link may be used to communicate with other externalvehicles in the vicinity of the vehicle or with local infrastructureelements. Cellular communication technologies may be used for long rangecommunication such as to external networks and if the cellularcommunication technology used have low latency it may also be used forcommunication between vehicles, vehicle to vehicle (V2V), and/or vehicleto infrastructure, V2X. Examples of cellular radio technologies are GSM,GPRS, EDGE, LTE, 5G, 5G NR, and so on, also including future cellularsolutions. However, in some solutions mid to short range communicationtechnologies are used such as Wireless Local Area (LAN), e.g. IEEE802.11 based solutions. ETSI is working on cellular standards forvehicle communication and for instance 5G is considered as a suitablesolution due to the low latency and efficient handling of highbandwidths and communication channels.

The present disclosure has been presented above with reference tospecific embodiments. However, other embodiments than the abovedescribed are possible and within the scope of the disclosure. Differentmethod steps than those described above, performing the method byhardware or software, may be provided within the scope of thedisclosure. Thus, according to an exemplary embodiment, there isprovided a non-transitory computer-readable storage medium storing oneor more programs configured to be executed by one or more processors ofa vehicle control system, the one or more programs comprisinginstructions for performing the method according to any one of theabove-discussed embodiments. In several aspects and embodiments, thereis provided a computer program product comprising instructions which,when the program is executed by one or more processors of a processingsystem, causes the processing system to carry out the method accordingto any one of the embodiments of the method of the present disclosure.

Alternatively, according to another exemplary embodiment a cloudcomputing system can be configured to perform any of the methodspresented herein. The cloud computing system may comprise distributedcloud computing resources that jointly perform the methods presentedherein under control of one or more computer program products.

Generally speaking, a computer-accessible medium may include anytangible or non-transitory storage media or memory media such aselectronic, magnetic, or optical media—e.g., disk or CD/DVD-ROM coupledto computer system via bus. The terms “tangible” and “non-transitory,”as used herein, are intended to describe a computer-readable storagemedium (or “memory”) excluding propagating electromagnetic signals, butare not intended to otherwise limit the type of physicalcomputer-readable storage device that is encompassed by the phrasecomputer-readable medium or memory. For instance, the terms“non-transitory computer-readable medium” or “tangible memory” areintended to encompass types of storage devices that do not necessarilystore information permanently, including for example, random accessmemory (RAM). Program instructions and data stored on a tangiblecomputer-accessible storage medium in non-transitory form may further betransmitted by transmission media or signals such as electrical,electromagnetic, or digital signals, which may be conveyed via acommunication medium such as a network and/or a wireless link.

The processor(s) 11 (associated with the control device 10) may be orinclude any number of hardware components for conducting data or signalprocessing or for executing computer code stored in memory 12. Thedevice 10 may have an associated memory 12, and the memory 12 may be oneor more devices for storing data and/or computer code for completing orfacilitating the various methods described in the present description.The memory may include volatile memory or non-volatile memory. Thememory 12 may include database components, object code components,script components, or any other type of information structure forsupporting the various activities of the present description. Accordingto an exemplary embodiment, any distributed or local memory device maybe utilized with the systems and methods of this description. Accordingto an exemplary embodiment the memory 12 is communicably connected tothe processor 11 (e.g., via a circuit or any other wired, wireless, ornetwork connection) and includes computer code for executing one or moreprocesses described herein.

It should be appreciated that the ego-vehicle 1 further comprises asensor interface 13 which may also provide the possibility to acquiresensor data directly or via dedicated sensor control circuitry 6 in thevehicle. The vehicle 1 also comprises a communication/antenna interface14 which may further provide the possibility to send output to a remotelocation (e.g. remote operator or control centre) by means of an antenna8. Moreover, some sensors in the vehicle may communicate with thecontrol device 10 using a local network setup, such as CAN bus, I2C,Ethernet, optical fibres, and so on. The communication interface 14 maybe arranged to communicate with other control functions of the vehicleand may thus be seen as control interface also; however, a separatecontrol interface (not shown) may be provided. Local communicationwithin the vehicle may also be of a wireless type with protocols such asWiFi, LoRa, Zigbee, Bluetooth, or similar mid/short range technologies.

Accordingly, it should be understood that parts of the describedsolution may be implemented either in the vehicle, in a system locatedexternal the vehicle, or in a combination of internal and external thevehicle; for instance in a server in communication with the vehicle, aso called cloud solution. In some examples, the ML algorithm may beimplemented in the processing circuitry 11. In some examples, sensordata may be sent to an external system, wherein the external systemcomprises the ML algorithm to select the single initialized filter. Thedifferent features and steps of the embodiments may be combined in othercombinations than those described.

It should be noted that the word “comprising” does not exclude thepresence of other elements or steps than those listed and the words “a”or “an” preceding an element do not exclude the presence of a pluralityof such elements. It should further be noted that any reference signs donot limit the scope of the claims, that the disclosure may be at leastin part implemented by means of both hardware and software, and thatseveral “means” or “units” may be represented by the same item ofhardware.

Although the figures may show a specific order of method steps, theorder of the steps may differ from what is depicted. In addition, two ormore steps may be performed concurrently or with partial concurrence.Such variation will depend on the software and hardware systems chosenand on designer choice. All such variations are within the scope of thedisclosure. Likewise, in some cases some of the software implementationsmay be accomplished with standard programming techniques with rule-basedlogic and other logic to accomplish the various connection steps,processing steps, comparison steps and decision steps. The abovementioned and described embodiments are only given as examples andshould not be limiting to the present disclosure. Other solutions, uses,objectives, and functions within the scope of the disclosure as claimedin the below described patent embodiments should be apparent for theperson skilled in the art.

1. A method for determining a state of a vehicle on a road portionhaving two or more lanes, the vehicle comprising an Automated DrivingSystem (ADS) feature, the method comprising: obtaining map dataassociated with the road portion; obtaining positioning data indicatinga pose of the vehicle on the road; obtaining sensor data from a sensorsystem of the vehicle; initializing a plurality of filters for the roadportion wherein one filter is initialized per lane of the road portionbased on the obtained map data, the obtained positioning data, and theobtained sensor data, wherein each filter indicates an estimated stateof the vehicle on the road portion; associating one or more sensor datapoint(s) in the obtained sensor data to a corresponding map-element ofthe obtained map data; determining one or more normalized similarityscore(s) between the associated obtained map data and the obtainedsensor data; determining one or more multivariate time-series data basedon the determined one or more normalized similarity score(s), whereineach multivariate time-series data is attributed to a correspondinginitialized filter among the plurality of initialized filters; andproviding the one or more multivariate time-series data as input to atrained machine-learning algorithm; wherein the trained machine learningalgorithm is configured for: determining a confidence probability valuefor each initialized filter of the plurality of initialized filters bymeans of a probabilistic classifier; selecting one of the initializedfilters, by comparing the confidence probability values determined foreach initialized filter in conjunction with one or more multi-objectiveoptimized coefficient(s), each optimized coefficient being indicative ofan optimization between a readiness performance indicator and anaccuracy performance indicator for selecting a single initialized filteras an output of the machine learning algorithm indicative of a currentstate of the vehicle on the road portion; wherein the method furthercomprises: controlling the ADS feature of the vehicle based on theselected initialized filter.
 2. The method according to claim 1, whereineach initialized filter is one of a Bayesian filter and a combination ofmultiple Bayesian filters.
 3. The method according to claim 2, whereineach Bayesian filter is one of Kalman Filter, Extended Kalman Filter(EKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), andParticle Filter (PF).
 4. The method according to claim 1, wherein theobtained sensor data comprises information about a state of one or moreother vehicles in the surrounding environment of the vehicle, lanemarker geometry, lane marker type, traffic sign information, roadbarrier information, and Inertial Measurement Unit (IMU) data.
 5. Themethod according to claim 1, wherein the method further comprisesdetermining one or more normalized similarity score(s) between theassociated obtained map data and the obtained sensor data by: computingan association cost value for each sensor data point of the one or moresensor data point(s) associated to a corresponding map element; andselecting a sensor data point and map-element combination having thesmallest association cost value.
 6. The method according to claim 1,wherein the method further comprises determining the one or moremultivariate time-series data based on the determined one or morenormalized similarity score(s) by obtaining one or more time-dependentfeature(s) of each determined normalized similarity score.
 7. The methodaccording to claim 1, wherein the trained machine learning algorithm isfurther configured for: sorting the determined confidence probabilityvalues for the plurality of the initialized filters based on theconfidence level of each determined confidence probability value.
 8. Themethod according to claim 1, wherein for each optimized coefficient: thereadiness performance indicator comprises any one of an availabilityperformance indicator comprising a proportion of the one or moremultivariate time-series data for which a selection of a singleinitialized filter is performed by the trained machine learningalgorithm; and an earliness performance indicator comprising an averagefraction passed of the one or more multivariate time-series data beforea selection of a single initialized filter is performed by the trainedmachine learning algorithm; and further for each optimized coefficient:the performance accuracy indicator comprises a proportion ofcorrectly-selected single initialized filters by the trained machinelearning algorithm, being indicative of the current state of the vehicleon the road portion.
 9. The method according to claim 1, wherein the mapdata comprises HD map data.
 10. A non-transitory computer-readablestorage medium storing one or more programs configured to be executed byone or more processors of an in-vehicle processing system, the one ormore programs comprising instructions for performing the methodaccording to claim
 1. 11. A system for determining a state of a vehicleon a road portion having two or more lanes, the vehicle comprising anAutomated Driving System (ADS) feature, and the system comprisingprocessing circuitry configured to: obtain map data associated with theroad portion; obtain positioning data indicating a pose of the vehicleon the road; obtain sensor data from a sensor system of the vehicle;initialize a plurality of filters for the road portion wherein onefilter is initialized per lane of the road portion based on the obtainedmap data, the obtained positioning data, and the obtained sensor data,wherein each filter indicates an estimated state of the vehicle on theroad portion; associate one or more sensor data point(s) in the obtainedsensor data to a corresponding map-element of the obtained map data;determine one or more normalized similarity score(s) between theassociated obtained map data and the obtained sensor data; determine oneor more multivariate time-series data based on the determined one ormore normalized similarity score(s), wherein each multivariatetime-series data is attributed to a corresponding initialized filteramong the plurality of initialized filters; and provide the one or moremultivariate time-series data as input to a trained machine-learningalgorithm; wherein the trained machine learning algorithm is configuredto: determine a confidence probability value for each initialized filterof the plurality of initialized filters by means of a probabilisticclassifier; select one of the initialized filters, by comparing theconfidence probability values determined for each initialized filter inconjunction with one or more multi-objective optimized coefficient(s),each optimized coefficient being indicative of an optimization between areadiness performance indicator and an accuracy performance indicatorfor selecting a single initialized filter as an output of the machinelearning algorithm indicative of a current state of the vehicle on theroad portion; wherein the processing circuitry is further configured to:control the ADS feature of the vehicle based on the selected initializedfilter.
 12. The system according to claim 11, wherein the processingcircuitry is further configured to: compute an association cost valuefor each sensor data point of the one or more sensor data point(s)associated to a corresponding map element; and select a sensor datapoint and map-element combination having the smallest association costvalue.
 13. The system according to claim 11, wherein the processingcircuitry is further configured to: determine the one or moremultivariate time-series data based on the determined one or morenormalized similarity score(s) by obtaining one or more time-dependentfeature(s) of each determined normalized similarity score.
 14. Thesystem according to claim 11, wherein for each optimized coefficient:the readiness performance indicator comprises any one of an availabilityperformance indicator comprising a proportion of the one or moremultivariate time-series data for which a selection of a singleinitialized filter is performed by the trained machine learningalgorithm; and an earliness performance indicator comprising an averagefraction passed of the one or more multivariate time-series data beforea selection of a single initialized filter is performed by the trainedmachine learning algorithm; and further for each optimized coefficient:the performance accuracy indicator comprises a proportion ofcorrectly-selected single initialized filters by the trained machinelearning algorithm, being indicative of the current state of the vehicleon the road portion.
 15. A vehicle comprising: one or morevehicle-mounted sensors configured to monitor a surrounding environmentof the vehicle; a localization system configured to monitor a pose ofthe vehicle on a road; and a system according to claim 11.